from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 10 predictions (41.67%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	library	diff_accuracy_scores	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
4	KNeighborsClassifier_brute_force	sklearn	0.067	predict	100000	1000	100	2.854584	0.035681	NaN	0.000280	0.002855	brute	-1	5	0.743	0.172763	0.001154	0.676	16.523125	16.523493
5	KNeighborsClassifier_brute_force	sklearn	1.000	predict	100000	1	100	0.023703	0.003192	NaN	0.000034	0.023703	brute	-1	5	1.000	0.008614	0.000261	0.000	2.751600	2.752866
7	KNeighborsClassifier_brute_force	sklearn	0.103	predict	100000	1000	100	2.093583	0.001914	NaN	0.000382	0.002094	brute	1	100	0.846	0.175009	0.000722	0.743	11.962707	11.962809
13	KNeighborsClassifier_brute_force	sklearn	0.103	predict	100000	1000	100	2.097554	0.011044	NaN	0.000381	0.002098	brute	1	5	0.743	0.209593	0.000434	0.846	10.007766	10.007788
16	KNeighborsClassifier_brute_force	sklearn	0.067	predict	100000	1000	100	1.172756	0.005058	NaN	0.000682	0.001173	brute	1	1	0.676	0.173695	0.000380	0.743	6.751800	6.751816
17	KNeighborsClassifier_brute_force	sklearn	1.000	predict	100000	1	100	0.018287	0.000186	NaN	0.000044	0.018287	brute	1	1	0.000	0.008849	0.000289	1.000	2.066672	2.067776
22	KNeighborsClassifier_brute_force	sklearn	0.038	predict	100000	1000	2	2.783096	0.015087	NaN	0.000006	0.002783	brute	-1	5	0.883	0.025507	0.000160	0.845	109.109544	109.111688
25	KNeighborsClassifier_brute_force	sklearn	0.004	predict	100000	1000	2	2.062517	0.004221	NaN	0.000008	0.002063	brute	1	100	0.887	0.026660	0.000170	0.883	77.364249	77.365823
31	KNeighborsClassifier_brute_force	sklearn	0.004	predict	100000	1000	2	2.045558	0.002533	NaN	0.000008	0.002046	brute	1	5	0.883	0.059798	0.000219	0.887	34.207766	34.207996
34	KNeighborsClassifier_brute_force	sklearn	0.038	predict	100000	1000	2	1.056716	0.002134	NaN	0.000015	0.001057	brute	1	1	0.845	0.026695	0.000103	0.883	39.585345	39.585640

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.0	7.165	0.0	-1	1	0.047	0.0	0.235	0.235	See	See
3	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.0	7.208	0.0	-1	5	0.047	0.0	0.234	0.234	See	See
6	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.0	7.341	0.0	1	100	0.047	0.0	0.232	0.232	See	See
9	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.0	7.313	0.0	-1	100	0.047	0.0	0.235	0.235	See	See
12	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.0	7.150	0.0	1	5	0.047	0.0	0.239	0.239	See	See
15	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.0	7.262	0.0	1	1	0.047	0.0	0.234	0.234	See	See
18	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.0	0.369	0.0	-1	1	0.008	0.0	0.527	0.528	See	See
21	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.0	0.378	0.0	-1	5	0.008	0.0	0.518	0.518	See	See
24	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.0	0.378	0.0	1	100	0.008	0.0	0.520	0.521	See	See
27	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.0	0.377	0.0	-1	100	0.008	0.0	0.518	0.518	See	See
30	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.0	0.376	0.0	1	5	0.008	0.0	0.521	0.521	See	See
33	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.0	0.373	0.0	1	1	0.008	0.0	0.521	0.522	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	1.944	0.052	0.000	0.002	-1	1	0.174	0.001	11.159	11.159	See	See
2	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.023	0.003	0.000	0.023	-1	1	0.009	0.000	2.691	2.693	See	See
4	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	2.855	0.036	0.000	0.003	-1	5	0.173	0.001	16.523	16.523	See	See
5	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.024	0.003	0.000	0.024	-1	5	0.009	0.000	2.752	2.753	See	See
7	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	2.094	0.002	0.000	0.002	1	100	0.175	0.001	11.963	11.963	See	See
8	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.019	0.000	0.000	0.019	1	100	0.008	0.000	2.253	2.255	See	See
10	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	2.910	0.037	0.000	0.003	-1	100	0.209	0.001	13.935	13.936	See	See
11	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.024	0.002	0.000	0.024	-1	100	0.009	0.000	2.721	2.723	See	See
13	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	2.098	0.011	0.000	0.002	1	5	0.210	0.000	10.008	10.008	See	See
14	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.019	0.000	0.000	0.019	1	5	0.009	0.000	2.268	2.269	See	See
16	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	1.173	0.005	0.001	0.001	1	1	0.174	0.000	6.752	6.752	See	See
17	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.018	0.000	0.000	0.018	1	1	0.009	0.000	2.067	2.068	See	See
19	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	1.789	0.027	0.000	0.002	-1	1	0.025	0.000	70.198	70.202	See	See
20	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.007	0.003	0.000	0.007	-1	1	0.001	0.000	9.583	9.741	See	See
22	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	2.783	0.015	0.000	0.003	-1	5	0.026	0.000	109.110	109.112	See	See
23	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.009	0.002	0.000	0.009	-1	5	0.001	0.000	11.797	12.031	See	See
25	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	2.063	0.004	0.000	0.002	1	100	0.027	0.000	77.364	77.366	See	See
26	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.936	4.027	See	See
28	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	2.797	0.028	0.000	0.003	-1	100	0.060	0.000	46.850	46.850	See	See
29	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.009	0.002	0.000	0.009	-1	100	0.001	0.000	11.359	11.575	See	See
31	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	2.046	0.003	0.000	0.002	1	5	0.060	0.000	34.208	34.208	See	See
32	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	3.569	3.632	See	See
34	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	1.057	0.002	0.000	0.001	1	1	0.027	0.000	39.585	39.586	See	See
35	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.470	2.519	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 8 predictions (33.33%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	library	diff_accuracy_scores	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_kd_tree	sklearn	0.011	predict	1000000	1000	10	1.701493	0.008678	NaN	0.000047	0.001701	kd_tree	1	5	0.975	0.116527	0.001456	0.964	14.601746	14.602887
4	KNeighborsClassifier_kd_tree	sklearn	0.002	predict	1000000	1000	10	0.950790	0.006334	NaN	0.000084	0.000951	kd_tree	-1	5	0.975	0.599678	0.013020	0.973	1.585500	1.585874
10	KNeighborsClassifier_kd_tree	sklearn	0.011	predict	1000000	1000	10	0.903230	0.004839	NaN	0.000089	0.000903	kd_tree	1	1	0.964	0.203214	0.002935	0.975	4.444735	4.445199
13	KNeighborsClassifier_kd_tree	sklearn	0.002	predict	1000000	1000	10	5.397381	0.102031	NaN	0.000015	0.005397	kd_tree	1	100	0.973	0.205844	0.001802	0.975	26.220706	26.221711
19	KNeighborsClassifier_kd_tree	sklearn	0.028	predict	1000	1000	2	0.020273	0.000212	NaN	0.000789	0.000020	kd_tree	1	5	0.923	0.000489	0.000124	0.895	41.490814	42.806381
22	KNeighborsClassifier_kd_tree	sklearn	0.004	predict	1000	1000	2	0.022752	0.000471	NaN	0.000703	0.000023	kd_tree	-1	5	0.923	0.004568	0.000212	0.919	4.980863	4.986206
28	KNeighborsClassifier_kd_tree	sklearn	0.028	predict	1000	1000	2	0.019136	0.000230	NaN	0.000836	0.000019	kd_tree	1	1	0.895	0.000743	0.000121	0.923	25.751095	26.090725
31	KNeighborsClassifier_kd_tree	sklearn	0.004	predict	1000	1000	2	0.033771	0.000218	NaN	0.000474	0.000034	kd_tree	1	100	0.919	0.000778	0.000161	0.923	43.387250	44.303833

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	3.048	0.072	0.026	0.0	1	5	0.757	0.004	4.025	4.025	See	See
3	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	3.900	0.025	0.021	0.0	-1	5	0.735	0.006	5.305	5.305	See	See
6	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	4.042	0.044	0.020	0.0	-1	1	0.756	0.008	5.348	5.348	See	See
9	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	4.103	0.099	0.019	0.0	1	1	0.715	0.004	5.735	5.736	See	See
12	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	4.141	0.022	0.019	0.0	1	100	0.764	0.005	5.420	5.420	See	See
15	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	3.587	0.060	0.022	0.0	-1	100	0.736	0.015	4.873	4.874	See	See
18	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.028	0.0	1	5	0.004	0.001	0.151	0.163	See	See
21	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	-1	5	0.002	0.001	0.346	0.445	See	See
24	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	-1	1	0.001	0.000	0.615	0.624	See	See
27	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	1	1	0.001	0.000	0.598	0.607	See	See
30	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	1	100	0.001	0.000	0.602	0.611	See	See
33	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	-1	100	0.001	0.000	0.622	0.631	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	1.701	0.009	0.000	0.002	1	5	0.117	0.001	14.602	14.603	See	See
2	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	5	0.000	0.000	5.789	6.504	See	See
4	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	0.951	0.006	0.000	0.001	-1	5	0.600	0.013	1.586	1.586	See	See
5	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.001	0.000	5.866	6.455	See	See
7	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	0.505	0.012	0.000	0.001	-1	1	0.121	0.002	4.187	4.188	See	See
8	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	12.022	13.583	See	See
10	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	0.903	0.005	0.000	0.001	1	1	0.203	0.003	4.445	4.445	See	See
11	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	4.397	4.886	See	See
13	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	5.397	0.102	0.000	0.005	1	100	0.206	0.002	26.221	26.222	See	See
14	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.000	0.000	10.193	11.325	See	See
16	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	3.209	0.028	0.000	0.003	-1	100	0.592	0.005	5.419	5.419	See	See
17	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	8.590	9.440	See	See
19	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	5	0.000	0.000	41.491	42.806	See	See
20	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	5.301	7.023	See	See
22	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.023	0.000	0.001	0.000	-1	5	0.005	0.000	4.981	4.986	See	See
23	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	16.918	21.755	See	See
25	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.021	0.001	0.001	0.000	-1	1	0.000	0.000	44.022	45.824	See	See
26	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	17.001	21.526	See	See
28	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.019	0.000	0.001	0.000	1	1	0.001	0.000	25.751	26.091	See	See
29	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.210	7.022	See	See
31	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.034	0.000	0.000	0.000	1	100	0.001	0.000	43.387	44.304	See	See
32	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.202	6.759	See	See
34	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.036	0.005	0.000	0.000	-1	100	0.005	0.000	7.759	7.778	See	See
35	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	17.681	22.543	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	sklearn	fit	1000000	1000000	2	0.567	0.006	30	0.028	0.0	k-means++	0.407	0.029	1.393	1.396	See	See
3	KMeans_tall	sklearn	fit	1000000	1000000	2	0.504	0.008	30	0.032	0.0	random	0.367	0.019	1.371	1.373	See	See
6	KMeans_tall	sklearn	fit	1000000	1000000	100	5.765	0.023	30	0.139	0.0	k-means++	2.775	0.011	2.077	2.077	See	See
9	KMeans_tall	sklearn	fit	1000000	1000000	100	5.516	0.022	30	0.145	0.0	random	2.718	0.059	2.029	2.030	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	sklearn	predict	1000000	1000	2	0.002	0.0	30	0.011	0.000	k-means++	0.0	0.0	10.532	13.013	See	See
2	KMeans_tall	sklearn	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	k-means++	0.0	0.0	11.065	14.109	See	See
4	KMeans_tall	sklearn	predict	1000000	1000	2	0.001	0.0	30	0.012	0.000	random	0.0	0.0	9.471	11.468	See	See
5	KMeans_tall	sklearn	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	random	0.0	0.0	10.662	13.261	See	See
7	KMeans_tall	sklearn	predict	1000000	1000	100	0.002	0.0	30	0.485	0.000	k-means++	0.0	0.0	6.115	6.803	See	See
8	KMeans_tall	sklearn	predict	1000000	1	100	0.001	0.0	30	0.001	0.001	k-means++	0.0	0.0	10.677	13.511	See	See
10	KMeans_tall	sklearn	predict	1000000	1000	100	0.002	0.0	30	0.486	0.000	random	0.0	0.0	6.103	6.800	See	See
11	KMeans_tall	sklearn	predict	1000000	1	100	0.001	0.0	30	0.001	0.001	random	0.0	0.0	10.727	13.577	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 3 predictions (37.5%).
The observed differences can be found in the diff_adjusted_rand_scores column.
The chosen difference threshold is 0.001 for adjusted_rand_score.
See details in the dataframe below.

	estimator	library	diff_adjusted_rand_scores	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup
4	KMeans_short	sklearn	0.001361	predict	10000	1000	2	0.001827	0.000184	20	0.008757	0.000002	full	k-means++	20	300	1	1.000000e-16	0.000744	0.000502	0.000100	-0.000617	3.642602	3.713657
7	KMeans_short	sklearn	0.002111	predict	10000	1000	100	0.002515	0.000278	20	0.318138	0.000003	full	random	20	300	1	1.000000e-16	0.340206	0.000996	0.000154	0.342317	2.525393	2.555525
10	KMeans_short	sklearn	0.016992	predict	10000	1000	100	0.002498	0.000259	20	0.320238	0.000002	full	k-means++	20	300	1	1.000000e-16	0.321348	0.001018	0.000152	0.338339	2.453443	2.480801

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	sklearn	fit	10000	10000	2	0.072	0.000	20	0.002	0.0	random	0.026	0.001	2.786	2.789	See	See
3	KMeans_short	sklearn	fit	10000	10000	2	0.209	0.000	20	0.001	0.0	k-means++	0.081	0.001	2.580	2.581	See	See
6	KMeans_short	sklearn	fit	10000	10000	100	0.191	0.001	20	0.042	0.0	random	0.105	0.002	1.816	1.816	See	See
9	KMeans_short	sklearn	fit	10000	10000	100	0.556	0.004	20	0.014	0.0	k-means++	0.312	0.009	1.784	1.785	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	sklearn	predict	10000	1000	2	0.002	0.001	20	0.008	0.000	random	0.000	0.0	4.137	4.207	See	See
2	KMeans_short	sklearn	predict	10000	1	2	0.001	0.000	20	0.000	0.001	random	0.000	0.0	10.228	12.475	See	See
4	KMeans_short	sklearn	predict	10000	1000	2	0.002	0.000	20	0.009	0.000	k-means++	0.001	0.0	3.643	3.714	See	See
5	KMeans_short	sklearn	predict	10000	1	2	0.001	0.000	20	0.000	0.001	k-means++	0.000	0.0	10.631	13.075	See	See
7	KMeans_short	sklearn	predict	10000	1000	100	0.003	0.000	20	0.318	0.000	random	0.001	0.0	2.525	2.556	See	See
8	KMeans_short	sklearn	predict	10000	1	100	0.001	0.000	20	0.001	0.001	random	0.000	0.0	8.577	9.967	See	See
10	KMeans_short	sklearn	predict	10000	1000	100	0.002	0.000	20	0.320	0.000	k-means++	0.001	0.0	2.453	2.481	See	See
11	KMeans_short	sklearn	predict	10000	1	100	0.001	0.000	20	0.001	0.001	k-means++	0.000	0.0	8.713	10.018	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	sklearn	fit	1000000	1000000	100	10.792	0.062	[20]	0.074	0.000	1.942	0.010	5.557	5.557	See	See
3	LogisticRegression	sklearn	fit	1000	1000	10000	0.785	0.022	[27]	0.102	0.001	0.957	0.031	0.820	0.821	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	sklearn	predict	1000000	1000	100	0.000	0.0	[20]	2.806	0.0	0.000	0.0	0.829	0.915	See	See
2	LogisticRegression	sklearn	predict	1000000	1	100	0.000	0.0	[20]	0.012	0.0	0.000	0.0	0.376	0.441	See	See
4	LogisticRegression	sklearn	predict	1000	100	10000	0.002	0.0	[27]	5.080	0.0	0.003	0.0	0.539	0.546	See	See
5	LogisticRegression	sklearn	predict	1000	1	10000	0.000	0.0	[27]	0.913	0.0	0.001	0.0	0.128	0.132	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	sklearn	fit	1000	1000	10000	0.158	0.001	0.506	0.0	0.180	0.010	0.877	0.878	See	See
3	Ridge	sklearn	fit	1000000	1000000	100	1.063	0.010	0.752	0.0	0.217	0.001	4.896	4.896	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	sklearn	predict	1000	1000	10000	0.012	0.0	6.746	0.0	0.019	0.0	0.611	0.611	See	See
2	Ridge	sklearn	predict	1000	1	10000	0.000	0.0	1.002	0.0	0.000	0.0	0.732	0.889	See	See
4	Ridge	sklearn	predict	1000000	1000	100	0.000	0.0	5.515	0.0	0.000	0.0	0.608	0.732	See	See
5	Ridge	sklearn	predict	1000000	1	100	0.000	0.0	0.013	0.0	0.000	0.0	0.655	0.812	See	See